On-line transaction and auctions systems are known. For example, eBay.com of San Jose, Calif. hosts an enormously popular on-line auction facility allowing users to auction and buy a wide variety of items, such as automobiles, electronics and services. Of course, there are a vast array of on-line auction facilities, some of which are highly specialized to a particular category, such as firearms, automobiles, and the like. To facilitate and promote their use, such on-line auction facilities offer tools and functionality to assist the user. For example, on-line auction facilities post buying and selling tips, as well as generalized descriptions of the on-line auction process and available options, such as reserve and “buy-it-now” prices. On-line auction facilities also offer tools that facilitate tracking and management of items being sold by a particular user.
Often times, it is useful for users desiring to post an item for auction to search for and analyze similar auctions. Furthermore, U.S. patent application Ser. Nos. 10/646,096 and 10/878,261 teach that users wishing to sell an item can benefit greatly from an analysis of the history of the selling prices, and other auction attributes, of similar items in the past. Yet, the on-line auction facilities generally offer very limited means of identifying auctions of items which are similar or identical to that of a contemplated auction. There is a need for generating that history by finding other auctions with a reasonably high probability of being a “similar” item.
On-line auction facilities generally offer a “keyword match” form of searching, wherein a “search engine” applies user-supplied keywords to search the on-line auction facility's database. Each auction item has a set of keywords assigned to it, some assigned by the auction facility but most assigned by the seller. The search engines use these keywords to perform an absolute match search (sometimes called match-all search) from their databases to retrieve a list of relevant objects. For example in FIG. 4, if the user enters the keywords “X”, “Y”, and “Z”, the search engine will return the objects that are in the tight triangle-like region labeled “X+Y+Z”.
There are keyword search engines that employed variants of this absolute keyword-search technique: 1) Regular expressions are used in conjunction with the keywords. For example, in FIG. 4, if the user enters “X” AND (“Y” OR “Z”), the keyword search engines will return the objects in the regions labeled “X+Y”, “X+Z”, and “X+Y+Z”; and 2) Keyword auto-correction. Some search engines take into consideration that the keywords entered by the users or being listed with the objects might be misspelled. The keyword auto-correction technique allows search engines to lookup similar keywords that resemble the spelling of these keywords and ask users to correct the searches.
Both techniques are used extensively and are very useful. However, there are two key issues that they cannot address:
1) The keywords are collections of phrases or words. Some of these phrases or words are perfectly substitutable for each other. For example, “phone” is equivalent to “telephone” in English. Similarly, a mobile phone might be called a “cell phone” or “cellular phone” or “hand phone” or “wireless telephone”. A search engine should identify all these as “the same object” or “similar objects” despite the different keywords. This is especially problematic for an Internet auction, wherein the keywords are largely entered by the sellers, who may or may not include all relevant keywords. The burden of locating these items is thus shifted to the buyers searching for them.
2) Sellers of auction objects take advantage of the absolute keyword search mechanism, and inject many irrelevant keywords in the descriptions of the items, so that the objects will be more readily found. These keywords are often irrelevant to the items, but rather are related to the auction facility's item categories. The incentive for a seller to engage in this practice is especially great because many potential buyers rely on the search engines to find items they wish to buy.
Because of the aforesaid problems associated with prior art keyword search mechanisms, it is difficult or impossible in current online auction facilities to efficiently identify a set of auctions that are similar, or otherwise relevant, to a contemplated or model auction. Furthermore, there is additional information maintained about auction items which could also be used to add precision to the search method: the seller's name or ID, the prices of past auctions, the auction facility's item categories, UPC codes, and more.
In light of the foregoing, a need in the art exists for methods, apparatuses and systems that facilitate the identification of a set of auctions which are similar to a contemplated auction. Embodiments of the present invention substantially fulfill this need.